----------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\research\china\decentralization\restat_data\tabdata\dofiles\tab_da
> ta\tabdata3.log
  log type:  text
 opened on:  22 Jul 2016, 09:50:17

. 
. 
. ******* 1. Ready 1990 Aggregate Census Data from Harvard & Merge to Loren's Data
>  ******************
. 
. use ..\..\data\census\source\chinaad190.dta, clear

. sort gbcenmq

. merge gbcenmq using ..\..\data\census\source\chinaae190.dta
(note: you are using old merge syntax; see [D] merge for new syntax)

. tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      2,438      100.00      100.00
------------+-----------------------------------
      Total |      2,438      100.00

. drop _merge

. gen countyCode = string(gbcenmq)

. *** These are missing
. replace a6 = 5 if substr(countyCode,-2,.)=="51"
(7 real changes made)

. replace a6 = 6 if substr(countyCode,-2,.)=="52"|substr(countyCode,-2,.)=="53"
(2 real changes made)

. *** This is a miscode based on the corr table
. replace a6 = 8 if gbcenmq==430312
(1 real change made)

. replace a6 = 8 if gbcenmq==410412
(1 real change made)

. *** These are strange locations
. drop if a6<0
(18 observations deleted)

. gen city_codec = 100*int(gbcenmq/100)

. **Consolidate few cases of multiple codes for the same prefecture
. replace city_codec = 110000 if city_codec==110100
(3 real changes made)

. replace city_codec = 110000 if city_codec==110200
(8 real changes made)

. replace city_codec = 120000 if city_codec==120100
(7 real changes made)

. replace city_codec = 120000 if city_codec==120200
(5 real changes made)

. replace city_codec = 310000 if city_codec==310100
(2 real changes made)

. replace city_codec = 310000 if city_codec==310200
(9 real changes made)

. replace city_codec = 500000 if city_codec==510200
(13 real changes made)

. save temp_census.dta, replace
(note: file temp_census.dta not found)
file temp_census.dta saved

. 
. *** Break off CP and aggregate, works since units are mutually exclusive
. keep if a6<=7
(2,180 observations deleted)

. #delimit cr
delimiter now cr
. sort city_codec gbcenmq

. by city_codec: replace nmcenmq = nmcenmq[1]
(52 real changes made)

. collapse (sum) a*, by(city_codec nmcenmq)

. rename city_codec city_code

. gen year=1990

. sort city_code year

. save ..\..\data\tabular_data_BJ\generated\cp90.dta, replace
file ..\..\data\tabular_data_BJ\generated\cp90.dta saved

. 
. *** Break off 2 obs for use later
. keep if city_code==430800|city_code==441400
(186 observations deleted)

. gen cemp_sect1 = a384+a390

. gen cemp_sect2 = a387

. gen cemp_sect3 = a381-cemp_sect2-cemp_sect1

. rename city_code city05

. keep city05 year cemp_sect1 cemp_sect2 cemp_sect3

. sort city05 year

. save tempcen90.dta, replace
(note: file tempcen90.dta not found)
file tempcen90.dta saved

. 
. *** Build PF Data
. use temp_census.dta

. sort city_codec gbcenmq

. by city_codec: replace nmcenmq = nmcenmq[1]
(2,076 real changes made)

. collapse (sum) a*, by(city_codec nmcenmq)

. rename city_codec city_code

. gen year=1990

. sort city_code year

. save ..\..\data\census\generated\pf90.dta, replace
file ..\..\data\census\generated\pf90.dta saved

. 
. *** Break off county level units (add back some prematurely promoted county citi
> es & qu)
. use temp_census.dta,clear

. #delimit ;
delimiter now ;
. keep if a6>6 | 
> gbcenmq==371101|gbcenmq==420401|gbcenmq==460151|gbcenmq==460251|gbcenmq==140602|
> gbcenmq==220502
> |gbcenmq==330402|gbcenmq==330902|gbcenmq==341002|gbcenmq==370502|gbcenmq==420802
> |gbcenmq==420851
> |gbcenmq==430703|gbcenmq==441802|gbcenmq==441901|gbcenmq==442001|gbcenmq==510802
> |gbcenmq==511102
> |gbcenmq==520201|gbcenmq==620402|gbcenmq==620502|gbcenmq==530201;
(174 observations deleted)

. #delimit cr
delimiter now cr
. *Deal with one a6=9 unit which is a county subset
. replace gbcenmq = 342421 if gbcenmq == 342401
(1 real change made)

. sort gbcenmq

. by gbcenmq: replace nmcenmq = nmcenmq[1]
(1 real change made)

. by gbcenmq: replace nmhanzi = nmhanzi[1]
(1 real change made)

. drop a5 a6

. collapse (sum) a* (mean) city_codec, by(gbcenmq nmcenmq nmhanzi)

. gen countyCode = string(gbcenmq)

. 
. ** Merge County units to Loren's Data
. **Recode a few units that do not match but align based on population
. replace countyCode = "220180" if countyCode=="220181"
(1 real change made)

. replace countyCode = "220383" if countyCode=="220381"
(1 real change made)

. replace countyCode = "220584" if countyCode=="220581"
(1 real change made)

. replace countyCode = "220585" if countyCode=="220582"
(1 real change made)

. replace countyCode = "230180" if countyCode=="230181"
(1 real change made)

. replace countyCode = "230880" if countyCode=="230881"
(1 real change made)

. replace countyCode = "320280" if countyCode=="320282"
(1 real change made)

. replace countyCode = "320380" if countyCode=="320381"
(1 real change made)

. replace countyCode = "320580" if countyCode=="320583"
(1 real change made)

. replace countyCode = "320680" if countyCode=="320681"
(1 real change made)

. replace countyCode = "320880" if countyCode=="320882"
(1 real change made)

. replace countyCode = "320980" if countyCode=="320981"
(1 real change made)

. replace countyCode = "321080" if countyCode=="321083"
(1 real change made)

. replace countyCode = "321180" if countyCode=="321181"
(1 real change made)

. replace countyCode = "350480" if countyCode=="350481"
(1 real change made)

. replace countyCode = "350580" if countyCode=="350581"
(1 real change made)

. replace countyCode = "360480" if countyCode=="360481"
(1 real change made)

. replace countyCode = "371198" if countyCode=="371101"
(1 real change made)

. replace countyCode = "410480" if countyCode=="410482"
(1 real change made)

. replace countyCode = "410780" if countyCode=="410782"
(1 real change made)

. replace countyCode = "410880" if countyCode=="410882"
(1 real change made)

. replace countyCode = "411080" if countyCode=="411081"
(1 real change made)

. replace countyCode = "420803" if countyCode=="420851"
(1 real change made)

. replace countyCode = "441998" if countyCode=="441901"
(1 real change made)

. replace countyCode = "442098" if countyCode=="442001"
(1 real change made)

. replace countyCode = "510180" if countyCode=="510181"
(1 real change made)

. replace countyCode = "510680" if countyCode=="510681"
(1 real change made)

. replace countyCode = "510780" if countyCode=="510781"
(1 real change made)

. replace countyCode = "511180" if countyCode=="511181"
(1 real change made)

. sort countyCode

. merge countyCode using ..\..\data\census\generated\count1990.dta
(note: you are using old merge syntax; see [D] merge for new syntax)

. tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        152        5.52        5.52
          2 |        507       18.42       23.95
          3 |      2,093       76.05      100.00
------------+-----------------------------------
      Total |      2,752      100.00

. rename _merge mrgcen

. sort countyCode

. 
. **  add transportation variable in 1990 (HY Aug 20, 2011)
. merge countyCode using ..\..\data\census\generated\transport1990.dta
(note: you are using old merge syntax; see [D] merge for new syntax)

. tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        152        5.52        5.52
          3 |      2,600       94.48      100.00
------------+-----------------------------------
      Total |      2,752      100.00

. drop _merge

. 
. gen census_code = real(countyCode)

. gen cencode = census_code

. gen province_code = 10000*int(census_code/10000)

. sort census_code

. save temp_census.dta, replace
file temp_census.dta saved

. 
. 
. *********** 2. Merge 1990 Census Data Sets to the Correspondence Table *********
> ************
. 
. use ..\..\data\correspondence_tables\generated\correspondence_82_10.dta

. keep if year==1990
(14,783 observations deleted)

. 
. *** Merge on RZ's codes
. sort unit_code_08

. merge unit_code_08 using ..\..\data\correspondence_tables\source\census_match90.
> dta
(note: you are using old merge syntax; see [D] merge for new syntax)

. tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         21        0.85        0.85
          2 |         21        0.85        1.71
          3 |      2,415       98.29      100.00
------------+-----------------------------------
      Total |      2,457      100.00

. 
. ** These are from the 2 prefectures we dropped
. drop if _merge==2
(21 observations deleted)

. *** These are added obs in which census_code is the same 
. replace census_code = unit_code_08 if _merge==1
(21 real changes made)

. drop _merge

. 
. *** Merge on Census data
. sort census_code

. merge census_code using temp_census.dta, update
(note: you are using old merge syntax; see [D] merge for new syntax)
(note: variable census_code was long, now double to accommodate using data's
       values)
(note: variable province_code was long, now double to accommodate using data's
       values)

. ***Drop counties in provinces outside of study area
. sort province_code _merge

. by province_code: gen fmrg = _merge[1]

. by province_code: gen lmrg = _merge[_N]

. drop if fmrg==2 & lmrg==2
(342 observations deleted)

. drop fmrg lmrg

. tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         95        3.79        3.79
          2 |         69        2.75        6.55
          3 |      2,303       91.94       98.48
          5 |         38        1.52      100.00
------------+-----------------------------------
      Total |      2,505      100.00

. tab _merge mrgcen

           |              mrgcen
    _merge |         1          2          3 |     Total
-----------+---------------------------------+----------
         2 |        12          4         53 |        69 
         3 |        35        454      1,814 |     2,303 
         5 |         0          7         31 |        38 
-----------+---------------------------------+----------
     Total |        47        465      1,898 |     2,410 


. *browse unit_code_08 census_code unit_name nmcen _merge mrgcen if _merge<3 | mrg
> cen<3
. 
. /** The rest have been handchecked and need not be matched 
> they are individual urban districts or outside of the study area **/
. drop if _merge==2
(69 observations deleted)

. drop _merge mrgcen

. 
. sort unit_code_08 year

. save temp_census.dta, replace
file temp_census.dta saved

. 
. 
. ********** 3. Merge Census and Tabular Data Together in 2000 and 2005 **********
> ****
. 
. **Ready sample census data from other years
. use ..\..\data\census\generated\count2000.dta, clear

. 
. **  add transportation variable in 2000 (HY Aug 20, 2011)
. sort countyCode

. merge countyCode using ..\..\data\census\generated\transport2000.dta
(note: you are using old merge syntax; see [D] merge for new syntax)

. tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      2,870      100.00      100.00
------------+-----------------------------------
      Total |      2,870      100.00

. drop _merge

. 
. gen year = 2000

. gen census_code = real(countyCode)

. drop countyCode

. 
. save cnt2000_temp.dta, replace
(note: file cnt2000_temp.dta not found)
file cnt2000_temp.dta saved

. 
. **  add transportation variable in 2005 (HY Aug 20, 2011)
. use ..\..\data\census\generated\count2005.dta, clear

. merge countyCode using ..\..\data\census\generated\transport2005.dta
(note: you are using old merge syntax; see [D] merge for new syntax)

. tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      2,867      100.00      100.00
------------+-----------------------------------
      Total |      2,867      100.00

. drop _merge

. save cnt2005_temp.dta, replace
(note: file cnt2005_temp.dta not found)
file cnt2005_temp.dta saved

. 
. **** Ready Year 2000-2005 Data
. use cnt2000_temp.dta, clear

. 
. append using cnt2005_temp.dta

. replace year = 2005 if year==.
(2,867 real changes made)

. replace census_code = countyCode if year==2005
(2,867 real changes made)

. gen cencode = census_code

. sort census_code year

. save cen0005.dta, replace
(note: file cen0005.dta not found)
file cen0005.dta saved

. 
. *Use US123, keep pop and codes in 1990, 2000 and 2005
. use ..\..\data\tabular_data_BJ\generated\us123.dta

. 
. sort unit_code_08 year

. merge unit_code_08 year using temp_census.dta
(note: you are using old merge syntax; see [D] merge for new syntax)
variables unit_code_08 year do not uniquely identify observations in the master
    data
(note: variable province_code was long, now double to accommodate using data's
       values)

. tab year _merge

           |        _merge
      year |         1          3 |     Total
-----------+----------------------+----------
      1982 |     2,352          0 |     2,352 
      1990 |         0      2,436 |     2,436 
      1995 |     2,469          0 |     2,469 
      2000 |     2,489          0 |     2,489 
      2005 |     2,491          0 |     2,491 
      2008 |     2,492          0 |     2,492 
      2010 |     2,490          0 |     2,490 
-----------+----------------------+----------
     Total |    14,783      2,436 |    17,219 


. drop _merge

. 
. /** Get the prefecture codes correct for 1990: Note city_codec is missing 
> for most urban districts, but city code is always correct in these cases**/
. replace city_code = city_codec if year==1990 & city_codec~=.
(529 real changes made)

. *** Leave subsequent years as are, though there may be some errors in 1990
. 
. /*** Make a guess at what census code might be for later years and merge on that
> , then
> try unit_code_08 instead.  Checks reveal no discrepancies between these two merg
> es
> so we can safely treat them as mutually exclusive. **/
. sort unit_code_08 year

. by unit_code_08: replace census_code = census_code[1]
(9690 real changes made, 520 to missing)

. replace census_code = 230109 if unit_code_08==230109 & year==2005
(1 real change made)

. replace census_code = unit_code_08 if census_code==.
(6,133 real changes made)

. sort census_code year

. merge census_code year using cen0005.dta, update
(note: you are using old merge syntax; see [D] merge for new syntax)
variables census_code year do not uniquely identify observations in the master
    data
(note: variable countyCode was long in the using data, but will be str6 now)

. tab _merge year if year==2000|year==2005

           |         year
    _merge |      2000       2005 |     Total
-----------+----------------------+----------
         1 |     1,010      1,196 |     2,206 
         2 |     1,391      1,572 |     2,963 
         4 |     1,479      1,295 |     2,774 
-----------+----------------------+----------
     Total |     3,880      4,063 |     7,943 


. move empTransport ruralMig

. save temp.dta, replace
file temp.dta saved

. 
. *** Break off obs that did not merge on
. keep if _merge==2
(17,219 observations deleted)

. drop _merge

. gen mrg1 = 1

. *** Change remaining census codes based on visual inspection
. replace census_code = 130207 if census_code==130282 & year==2000
(1 real change made)

. replace census_code = 141102 if census_code==142302 & year==2000
(1 real change made)

. replace census_code = 141181 if census_code==142301 & year==2000
(1 real change made)

. replace census_code = 141182 if census_code==142303 & year==2000
(1 real change made)

. replace census_code = 220605 if census_code==220625
(2 real changes made)

. replace census_code = 230109 if census_code==230107 & year==2005
(0 real changes made)

. replace census_code = 230112 if census_code==230181
(2 real changes made)

. replace census_code = 320205 if census_code==320283 & year==2000
(1 real change made)

. replace census_code = 320206 if census_code==320212 & year==2000
(1 real change made)

. replace census_code = 320412 if census_code==320483 & year==2000
(1 real change made)

. replace census_code = 320506 if census_code==320586 & year==2000
(1 real change made)

. replace census_code = 320803 if census_code==320882 & year==2000
(1 real change made)

. replace census_code = 320903 if census_code==320928 & year==2000
(1 real change made)

. replace census_code = 321311 if census_code==321321 & year==2000
(1 real change made)

. replace census_code = 330110 if census_code==330184 & year==2000
(1 real change made)

. replace census_code = 330502 if census_code==330501 & year==2005
(1 real change made)

. replace census_code = 340208 if census_code==340204 & year==2005
(1 real change made)

. replace census_code = 341600 if census_code==341602 & year==2000
(1 real change made)

. replace census_code = 370911 if census_code==370903
(2 real changes made)

. replace census_code = 410311 if census_code==410307
(2 real changes made)

. replace census_code = 420506 if census_code==420521 & year==2000
(1 real change made)

. replace census_code = 440513 if census_code==440582 & year==2000
(1 real change made)

. replace census_code = 440515 if census_code==440583 & year==2000
(1 real change made)

. replace census_code = 440560 if census_code==440510 & year==2000
(1 real change made)

. replace census_code = 440561 if census_code==440509 & year==2000
(1 real change made)

. replace census_code = 440511 if census_code==440508 & year==2000
(1 real change made)

. replace census_code = 440605 if census_code==440681 & year==2000
(1 real change made)

. replace census_code = 440606 if census_code==440682 & year==2000
(1 real change made)

. replace census_code = 440607 if census_code==440683 & year==2000
(1 real change made)

. replace census_code = 440608 if census_code==440684 & year==2000
(1 real change made)

. replace census_code = 440705 if census_code==440782 & year==2000
(1 real change made)

. replace census_code = 441303 if census_code==441381 & year==2000
(1 real change made)

. replace census_code = 441900 if census_code==441901
(2 real changes made)

. replace census_code = 442000 if census_code==442001
(2 real changes made)

. replace census_code = 451102 if census_code==452402 & year==2000
(1 real change made)

. replace census_code = 451281 if census_code==452702 & year==2000
(1 real change made)

. replace census_code = 500115 if census_code==500221 & year==2000
(1 real change made)

. replace census_code = 500116 if census_code==500381
(2 real changes made)

. replace census_code = 500117 if census_code==500382
(2 real changes made)

. replace census_code = 500118 if census_code==500383
(2 real changes made)

. replace census_code = 500119 if census_code==500384
(2 real changes made)

. replace census_code = 510903 if census_code==510902
(1 real change made)

. replace census_code = 530502 if census_code==533001 & year==2000
(1 real change made)

. replace census_code = 530801 if census_code==532701 & year==2000
(1 real change made)

. replace census_code = 640325 if census_code==640303 & year>=2000
(2 real changes made)

. replace census_code = 640502 if census_code==640321 & year==2000
(1 real change made)

. replace census_code = 640521 if census_code==640322  & year==2000
(1 real change made)

. sort mrg1 census_code year

. save cen0005.dta, replace
file cen0005.dta saved

. 
. 
. **************** 4. Ready 2010 Census Data **********************
. 
. clear

. foreach longshort in A L {
  2. 
.         clear
  3.         import excel "..\..\data\census\source\2010CountyCensus`longshort'.xl
> sx", sheet("Sheet1") firstrow
  4. 
.         * drop vars in Chinese 
.         drop County_CH City_CH Prov_CH
  5. 
.         *** drop provinces which are out of study area
.         *** - Neimenggu Hainan Xizang Qinghai Xinjiang
.         replace GbProv = "62" if GbCounty=="620201"
  6.         foreach x of num 15 46 47 54 63 65 {
  7.         drop if GbProv == "`x'"
  8.         }
  9. 
.         *** drop out of study area
. 
.         /*
>         GbCity  City_EN GbProv  Prov_EN
> 
>         5323    Chuxiongyizu    53      Yunnan
>         5325    Honghehanizuyizu        53      Yunnan
>         5326    Wenshanzhuangzumiaozu   53      Yunnan
>         5328    Xishuangbannadaizu      53      Yunnan
>         5329    Dalibaizu       53      Yunnan
>         5331    Dehongdaizujingpozu     53      Yunnan
>         5333    Nujianglisuzu   53      Yunnan
>         5334    Diqingcangzu    53      Yunnan
> 
>         6401    Yinchuan        64      Ningxia
>         6402    Shizuishan      64      Ningxia
> 
>         */
. 
.         foreach x of num 5323 5325 5326 5328 5329 5331 5333 5334 6401 6402 {
 10.         drop if GbCity == "`x'"
 11.         }
 12.         order GbCity City_EN
 13.         order GbProv Prov_EN
 14. 
.         sort GbCounty
 15.         save "2010CountyCensus`longshort'", replace
 16. }
(1 real change made)
(101 observations deleted)
(24 observations deleted)
(1 observation deleted)
(73 observations deleted)
(46 observations deleted)
(98 observations deleted)
(10 observations deleted)
(13 observations deleted)
(8 observations deleted)
(3 observations deleted)
(12 observations deleted)
(5 observations deleted)
(4 observations deleted)
(3 observations deleted)
(6 observations deleted)
(3 observations deleted)
(note: file 2010CountyCensusA.dta not found)
file 2010CountyCensusA.dta saved
(1 real change made)
(101 observations deleted)
(24 observations deleted)
(1 observation deleted)
(73 observations deleted)
(46 observations deleted)
(98 observations deleted)
(10 observations deleted)
(13 observations deleted)
(8 observations deleted)
(3 observations deleted)
(12 observations deleted)
(5 observations deleted)
(4 observations deleted)
(3 observations deleted)
(6 observations deleted)
(3 observations deleted)
(note: file 2010CountyCensusL.dta not found)
file 2010CountyCensusL.dta saved

. 
. 
. *---------------------------------------------------------------
. * Part 1.2: merge following two data sets together
. * - 2010CountyCensusA.dta
. * - 2010CountyCensusL.dta
. *---------------------------------------------------------------
. 
. merge 1:1 GbCounty using "2010CountyCensusA" // all counties are "_merge==3"

    Result                           # of obs.
    -----------------------------------------
    not matched                             0
    matched                             2,462  (_merge==3)
    -----------------------------------------

. 
. drop _merge

. 
. *---------------------------------------------------------------
. * Part 1.3: change variable names and keep only variables of interest
. * - resulting data set is 
. * census_county2010m.dta
. *---------------------------------------------------------------
. 
. // "original name of the variable" "our variable name" "label of the variable"
. 
. #delimit ;
delimiter now ;
. foreach x in 
> "A100001        a102    @Total Population"
> "A100002        a103    @Total Males"
> "A100003        a104    @Total Females"
> "A100008        a117    @Urban Population"
> "A100009        a120    @Rural Population"
> 
> "A200001        a185a   @Total Males at 0 Age"
> "A200002        a186a   @Total Females at 0 Age"
> "A200003        a185b   @Total Males at 1-4 Age"
> "A200004        a186b   @Total Females at 1-4 Age"
> "A200005        a188    @Total Males at 5-9 Age"
> "A200006        a189    @Total Females at 5-9 Age"
> "A200007        a191    @Total Males at 10-14 Age"
> "A200008        a192    @Total Females at 10-14 Age"
> "A200009        a194    @Total Males at 15-19 Age"
> "A200010        a195    @Total Females at 15-19 Age"
> 
> "A200025        aN2025  @Total Males at 55-59 Age"
> "A200026        aN2026  @Total Females at 55-59 Age"
> "A200027        aN2027  @Total Males at 60-64 Age"
> "A200028        aN2028  @Total Females at 60-64 Age"
> "A200029        aN2029  @Total Males at 65-69 Age"
> "A200030        aN2030  @Total Females at 65-69 Age"
> "A200031        aN2031  @Total Males at 70-74 Age"
> "A200032        aN2032  @Total Females at 70-74 Age"
> "A200033        aN2033  @Total Males at 75-79 Age"
> "A200034        aN2034  @Total Females at 75-79 Age"
> "A200035        aN2035  @Total Males at 80-84 Age"
> "A200036        aN2036  @Total Females at 80-84 Age"
> "A200037        aN2037  @Total Males at 85 Age and over"
> "A200038        aN2038  @Total Females at 85 Age and over"
> 
> 
> "A300012        aN3012  @Total Population of Migration from the Same County "
> "A300013        aN3013  @Total Population of Migration from Other Counties in th
> e Same Province"
> "A300014        aN3014  @Total Population of Migration from Other Provinces"
> 
> "A400003        a280    @Total Male with Primary School Education"
> "A400004        a281    @Total Female with Primary School Education"
> "A400005        a277    @Total Male with Junior Middle School Education"
> "A400006        a278    @Total Female with Junior Middle School Education"
> "A400007        a271_274        @Total Male with Senior High School Education"
> "A400008        a272_275        @Total Female with Senior High School Education"
> "A400009        a268    @Total Male with Junior College Education"
> "A400010        a269    @Total Female with Junior College Education"
> "A400011        a265    @Total Male with University and above Education"
> "A400012        a266    @Total Female with University and above Education"
> "A400013        aN4013  @Average Education Years"
> 
> "L500003        a434    @Sub-Total Employed Pop. of Responsible Persons of Gover
> nment Offices,Central Committee of The Communist Party of China ,Different Local
>  Organizations,Insfitution Unit and Enterprise"
> "L500004        a431    @Sub-Total Employed Pop. of  Professional/Technical"
> "L500005        a437    @Sub-Total Employed Pop. of  Clerk and Related Workers"
> "L500006        aN5006  @Sub-Total Employed Pop. of  Commerce,Service Trade Pers
> onnel"
> "L500007        a446    @Sub-Total Employed Pop. of�Crop Cultivation Production,
> �Forestry Production,�Animal Husbandry Production,�Fishery Production�and Fisher
> y Production Personnel"
> "L500008        a449    @Sub-Total Employed Pop. of  Production,Transport Equipm
> ent Operators and Related Personnel"
> "L500009        a451    @Sub-Total Employed Pop. of  Not Stated"
> 
> "L600002        a381    @Total Employed Population by Industry"
> "L600003        aN6003  @Sub-Total Employed Pop. in Agricultural Industry"
> "L600004        aN6004  @Sub-Total Employed Pop. in Mining Industry"
> "L600005        aN6005  @Sub-Total Employed Pop. in Manufacturing Industry"
> "L600006        aN6006  @Sub-Total Employed Pop. in Production and Supply of Ele
> ctric Power, Gas and Water Industry"
> "L600007        aN6007  @Sub-Total Employed Pop. in Construction Industry"
> "L600008        aN6008  @Sub-Total Employed Pop. in Storage and Postal Industry"
> "L600009        aN6009  @Sub-Total Employed Pop. in Transportation, Computer Ser
> vices and Software Industry "
> "L600010        aN6010  @Sub-Total Employed Pop. in Information Transfer, Wholes
> ale and Retail Trade Industry "
> "L600011        aN6011  @Sub-Total Employed Pop. in Hotel and Restaurants Indust
> ry"
> "L600012        aN6012  @Sub-Total Employed Pop. in Financial Industry"
> "L600013        aN6013  @Sub-Total Employed Pop. in Real Estate Industry"
> "L600014        aN6014  @Sub-Total Employed Pop. in Leasing and Business Service
> s Industry"
> "L600015        aN6015  @Sub-Total Employed Pop. in Scientific Research and Poly
> technic Services and Geological Prospecting Industry"
> "L600016        aN6016  @Sub-Total Employed Pop. in Water Conservancy, Environme
> nt and Public Facilities Management Industry"
> "L600017        aN6017  @Sub-Total Employed Pop. in Resident and Other Services 
> Industry"
> "L600018        aN6018  @Sub-Total Employed Pop. in Education Industry"
> "L600019        aN6019  @Sub-Total Employed Pop. in Health Care, Social Security
>  and Social Welfare"
> "L600020        aN6020  @Sub-Total Employed Pop. in Culture, Sports and Entertai
> nment Industry"
> "L600021        aN6021  @Sub-Total Employed Pop. in Public Administration and So
> cial Organizations"
> "L600022        aN6022  @Sub-Total Employed Pop. in International Organizations"
> 
> {;
  2. local censusID = word("`x'", 1);
  3. local ourID = word("`x'", 2);
  4. local ourLabel = substr("`x'", strpos("`x'", "@")+1,.);
  5. ren `censusID' `ourID' ;
  6. label var `ourID' "`ourLabel'" ;
  7. };
note: label truncated to 80 characters
note: label truncated to 80 characters
note: label truncated to 80 characters
note: label truncated to 80 characters
note: label truncated to 80 characters
note: label truncated to 80 characters
note: label truncated to 80 characters
note: label truncated to 80 characters

. #delimit cr
delimiter now cr
. 
. *** a185 def. is male pop age 0-4
. *** so add male age 0 + male age 1-4, same for female
. 
. gen a185 = a185a + a185b

. label var a185 "Total Males at 0-4 Age"

. gen a186 = a186a + a186b

. label var a186 "Total Females at 0-4 Age"

. drop a185a a185b a186a a186b

. 
. *** add another age category: Age 55 or older (aN_*_55older)
. 
. gen aN_m_55older = aN2025 + aN2027 + aN2029 + aN2031 + aN2033 + aN2035 + aN2037

. gen aN_f_55older = aN2026 + aN2028 + aN2030 + aN2032 + aN2034 + aN2036 + aN2038

. gen aN_t_55older = aN_m_55older + aN_f_55older

. 
. label var aN_t_55older "Total Population at Age 55 or older"

. label var aN_m_55older "Total Males at Age 55 or older"

. label var aN_f_55older "Total Females at Age 55 or older"

. 
. *** generate male + female variables for population by age categories
. 
. foreach x of num 184 187 190 193 {
  2. local x1 = `x'+1
  3. local x2 = `x'+2
  4. 
. gen a`x' = a`x1' + a`x2'
  5. }

. 
. label var a184 "Total Population at 0-4 Age"

. label var a187 "Total Population at 5-9 Age"

. label var a190 "Total Population at 10-14 Age"

. label var a193 "Total Population at 15-19 Age"

. 
. *** drop unneccessary variables
. drop A*

. drop L*

. drop aN2025-aN2038

. 
. gen unit_code_08 = regexs(1) if regexm(GbCounty, "(^[0-9][0-9][0-9][0-9][0-9][0-
> 9])")

. destring unit_code_08, replace
unit_code_08 has all characters numeric; replaced as long

. gen year = 2010

. sort unit_code_08 year

. save cnt2010_temp.dta, replace
(note: file cnt2010_temp.dta not found)
file cnt2010_temp.dta saved

. 
. 
. **************** 5. Ready 1982 Census Data **********************
. 
. ** Ready additional census data sets
. clear

. insheet using ..\..\data\census\source\population1982&2000_county.csv
(8 vars, 7,314 obs)

. ** Drop counties with no info
. drop if actpop==.
(4,689 observations deleted)

. keep unit_code_08 year actpop

. **These are CP level data integrated elsewhere
. drop if unit_code_08==-9999|unit_code_08==.
(10 observations deleted)

. replace year = 1982 if year==1983
(0 real changes made)

. replace year = 2000 if year==2001
(0 real changes made)

. sort unit_code_08 year

. save temp820005.dta, replace
(note: file temp820005.dta not found)
file temp820005.dta saved

. 
. clear

. use ..\..\data\census\generated\count1982.dta

. 
. gen year = 1982

. gen double unit_code_08 = real(countyCode)

. 
. *** Two counties span 2005 definition prefectures.  Split them evenly
. gen expnd = 1

. replace expnd = 2 if unit_code_08==412632 | unit_code_08==620121
(2 real changes made)

. replace unit_code_08 = unit_code_08+.1 if expnd==2
(2 real changes made)

. foreach X of varlist totalPop-male19to55WorkCollege {
  2.    replace `X' = `X'/2 if expnd==2
  3. }
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(0 real changes made)
(2 real changes made)
(2 real changes made)
(1 real change made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(1 real change made)
(2 real changes made)
(1 real change made)
(2 real changes made)
(1 real change made)
(2 real changes made)
(1 real change made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(0 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(2 real changes made)
(0 real changes made)

. expand expnd
(2 observations created)

. 
. sort unit_code_08 year

. save temp82.dta, replace
(note: file temp82.dta not found)
file temp82.dta saved

. 
. use temp.dta, clear

. rename _merge _m

. sort unit_code_08 year

. save temp_1.dta, replace
(note: file temp_1.dta not found)
file temp_1.dta saved

. 
. 
. ********** 6. Merge Census Data Sets to the Correspondence Table ***************
> ******
. 
. use ..\..\data\correspondence_tables\generated\correspondence_82_10.dta

. keep if year==2010
(14,729 observations deleted)

. 
. *** Merge on RZ's codes
. sort unit_code_08 year

. merge unit_code_08 year using cnt2010_temp.dta
(note: you are using old merge syntax; see [D] merge for new syntax)
(note: variable year was int, now float to accommodate using data's values)

. tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         28        1.12        1.12
          3 |      2,462       98.88      100.00
------------+-----------------------------------
      Total |      2,490      100.00

. ** 1s are all special districts with no census data available
. drop _merge

. 
. *** Merge on Census data
. sort unit_code_08 year

. merge unit_code_08 year using temp_1.dta, update
(note: you are using old merge syntax; see [D] merge for new syntax)
(note: variable city_code was long, now double to accommodate using data's
       values)
(note: variable province_code was long, now double to accommodate using data's
       values)
(note: variable city_name was str29, now str34 to accommodate using data's
       values)
(note: variable unit_name was str55, now str59 to accommodate using data's
       values)
(note: variable unit_status was byte, now float to accommodate using data's
       values)
(note: variable city05 was float, now double to accommodate using data's values)
(note: variable a102 was long, now double to accommodate using data's values)
(note: variable a103 was long, now double to accommodate using data's values)
(note: variable a104 was long, now double to accommodate using data's values)
(note: variable a117 was long, now double to accommodate using data's values)
(note: variable a120 was long, now double to accommodate using data's values)
(note: variable a184 was float, now double to accommodate using data's values)
(note: variable a185 was float, now double to accommodate using data's values)
(note: variable a186 was float, now double to accommodate using data's values)
(note: variable a187 was float, now double to accommodate using data's values)
(note: variable a188 was long, now double to accommodate using data's values)
(note: variable a189 was long, now double to accommodate using data's values)
(note: variable a190 was float, now double to accommodate using data's values)
(note: variable a191 was long, now double to accommodate using data's values)
(note: variable a192 was long, now double to accommodate using data's values)
(note: variable a193 was float, now double to accommodate using data's values)
(note: variable a194 was long, now double to accommodate using data's values)
(note: variable a195 was long, now double to accommodate using data's values)
(note: variable a265 was long, now double to accommodate using data's values)
(note: variable a268 was long, now double to accommodate using data's values)
(note: variable a269 was long, now double to accommodate using data's values)
(note: variable a277 was long, now double to accommodate using data's values)
(note: variable a278 was long, now double to accommodate using data's values)
(note: variable a280 was long, now double to accommodate using data's values)
(note: variable a281 was long, now double to accommodate using data's values)
(note: variable a381 was long, now double to accommodate using data's values)
(note: variable a431 was long, now double to accommodate using data's values)
(note: variable a434 was int, now double to accommodate using data's values)
(note: variable a437 was long, now double to accommodate using data's values)
(note: variable a446 was long, now double to accommodate using data's values)
(note: variable a449 was long, now double to accommodate using data's values)
(note: variable a451 was int, now double to accommodate using data's values)
variables unit_code_08 year do not uniquely identify observations in temp_1.dta

. 
. tab _merge if year==2010 /*Five units in Shantou should be included in 2010*/

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      2,402       96.47       96.47
          5 |         88        3.53      100.00
------------+-----------------------------------
      Total |      2,490      100.00

. drop _merge

. rename _m _merge

. 
. save temp.dta, replace
file temp.dta saved

. 
. ** Try merging again for the units that didn't merge
. use temp.dta

. drop if _merge==2
(2,963 observations deleted)

. replace census_code = unit_code_08 if _merge==1 & year>1990
(5,683 real changes made)

. gen mrg1 = _merge==1 & (year==2000|year==2005)

. drop _merge

. sort mrg1 census_code year 

. merge mrg1 census_code year using cen0005.dta, update
(note: you are using old merge syntax; see [D] merge for new syntax)
variables mrg1 census_code year do not uniquely identify observations in the
    master data

. tab _merge year if (year==2000|year==2005) & mrg1==1 & unit_status>0

           |         year
    _merge |      2000       2005 |     Total
-----------+----------------------+----------
         2 |       404        404 |       808 
         4 |       987      1,168 |     2,155 
-----------+----------------------+----------
     Total |     1,391      1,572 |     2,963 


. ** Drop _merge=2 obs b/c they are outside of study area or couldn't be matched
. drop if _merge==2
(808 observations deleted)

. drop mrg1 _merge

. 
. *** Clean up census and city codes
. drop census_code

. rename cencode census_code

. drop city_codec

. 
. *** Merge on 1982 Sample Data
. sort unit_code_08 year

. merge unit_code_08 year using temp82.dta, update
(note: you are using old merge syntax; see [D] merge for new syntax)
variables unit_code_08 year do not uniquely identify observations in the master
    data
variables unit_code_08 year do not uniquely identify observations in temp82.dta

. tab year _merge

           |              _merge
      year |         1          2          4 |     Total
-----------+---------------------------------+----------
      1982 |         0        401      2,352 |     2,753 
      1990 |     2,436          0          0 |     2,436 
      1995 |     2,469          0          0 |     2,469 
      2000 |     2,489          0          0 |     2,489 
      2005 |     2,491          0          0 |     2,491 
      2008 |     2,492          0          0 |     2,492 
      2010 |     2,490          0          0 |     2,490 
-----------+---------------------------------+----------
     Total |    14,867        401      2,352 |    17,620 


. 
. *Should have _merge=2 units in 1982 that are outside our study area
. *** These are out of our sample area or involve units split across prefectures
. drop if _merge==2
(401 observations deleted)

. drop _merge

. 
. *** Merge on 820005 Full Population Data
. sort unit_code_08 year

. merge unit_code_08 year using temp820005.dta, update
(note: you are using old merge syntax; see [D] merge for new syntax)
variables unit_code_08 year do not uniquely identify observations in the master
    data

. tab year _merge

           |        _merge
      year |         1          3 |     Total
-----------+----------------------+----------
      1982 |     2,203        149 |     2,352 
      1990 |     2,436          0 |     2,436 
      1995 |     2,469          0 |     2,469 
      2000 |        23      2,466 |     2,489 
      2005 |     2,491          0 |     2,491 
      2008 |     2,492          0 |     2,492 
      2010 |     2,490          0 |     2,490 
-----------+----------------------+----------
     Total |    14,604      2,615 |    17,219 


. ** these are special districts or uncollected data
. drop _merge

. 
. foreach list in                 "totalPop                                       
> c_totalPop                              census total pop"   ///
>                                 "emp                                     c_emp  
>                 census total emp"   ///
>                                 "empAgr                              c_emp_ag   
>        census agricultural emp"   ///
>                                 "empMining                                      
>  c_emp_min       census mining emp"   ///
>                                 "empManu                                        
>  c_emp_man                      census manufacturing emp"   ///
>                                 "empManuTrad                                c_em
> p_man_trad           census manu trad emp"   ///
>                                 "empManuChem                             c_emp_m
> an_chem        census manu chem emp"   ///
>                                 "empManuHeavy                            c_emp_m
> an_hvy            census manu heavy emp"   ///
>                                 "empManuHighTech                      c_emp_man_
> hitech       census manu hightech emp"   ///
>                                 "empUtil                              c_emp_util
>                          census util emp"   ///
>                                 "empCons                           c_emp_cons   
>                    census cons emp"   ///
>                                 "empSoft                            c_emp_soft  
>                      census soft emp"   ///
>                                 "empWhole                                       
> c_emp_whsl                      census wholesale emp"   ///
>                                 "empFina                            c_emp_fina  
>                       census finance emp"   ///
>                                 "empRes                             c_emp_res   
>                       census res emp"   ///
>                                 "empCul                              c_emp_cul  
>                         census cul emp"   ///
>                                 "empPub                             c_emp_pub   
>                     census pub emp"   ///
>                                                                 "empTransport   
>                           c_emp_tran                       census transportation
>  emp"   ///
>                                 "ruralMig                             c_rur_Mig 
>           census rural migrant"   ///
>                                 "urbanMig                          c_urb_Mig    
>       census urban migrant"   ///
>                                 "ruralMig19to55                               c_
> rur_mig_19to55_        census rural migrant 19-55"   ///
>                                 "ruralMig19to55High                      c_rur_m
> ig_19to55_h      census rural migrant 19-55 high"   ///
>                                 "urbanMig19to55                             c_ur
> b_mig_19to55_       census urban migrant 19-55"   ///
>                                 "urbanMig19to55High                    c_urb_mig
> _19to55_h     census urban migrant 19-55 high"   ///
>                                 "female19to55                                  c
> _f_19to55                         census female 19-55"   ///
>                                 "femaleUnder19                              c_f_
> Under19                     census female <19"   ///
>                                 "femaleOver55                                 c_
> f_Over55                        census female >55"   ///
>                                 "female19to55High         c_f_19to55_h          
>           census female 19-55 high"   ///
>                                 "female19to55Work                       c_f_19to
> 55_w_                census female 19-55 work"   ///
>                                 "female19to55WorkHigh                           
>    c_f_19to55_w_h                              census female 19-55 work high"   
> ///
>                                 "female19to55WorkCollege        c_f_19to55_w_c  
>                             census female19-55 work college"   ///
>                                 "male19to55                                     
>  c_m_19to55      census male 19-55"   ///
>                                 "maleUnder19                                  c_
> m_Under19                   census male <19"   ///
>                                 "maleOver55                                     
> c_m_Over55                      census male >55"   ///
>                                 "male19to55High                             c_m_
> 19to55_h                  census male 19-55 high"   ///
>                                 "male19to55Work                           c_m_19
> to55_w_                              census male 19-55 work"   ///
>                                 "male19to55WorkHigh                  c_m_19to55_
> w_h           census male 19-55 work high"   ///
>                                 "male19to55WorkCollege            c_m_19to55_w_c
>             census male 19-55 work college"   ///
>                                 "ruralMigA                          c_rur_mig_A 
>                      census rural migrantA"   ///
>                                 "urbanMigA                                      
>  c_urb_mig_A                     census urban migrant A"   ///
>                                 "ruralMig19to55A                            c_ru
> r_mig_19to55_A     census rural migrant 19-55 A"   ///
>                                 "ruralMig19to55HighA                   c_rur_mig
> _19to55_h_A    census rural migrant 19-55 high A"   ///
>                                 "urbanMig19to55A                          c_urb_
> mig_19to55_A    census urban migrant 19-55 A"   ///
>                                 "urbanMig19to55HighA                            
>      c_urb_mig_19to55_h_A               census urban migrant 19-55 high A"   ///
>                                 "ruralOutBirth                                  
>  c_rur_ob_                           census rural out birth"   ///
>                                 "urbanOutBirth                                 c
> _urb_ob_                          census urban out birth"   ///
>                                 "ruralOutBirth19to55      c_rur_ob_19to55_      
>     census rural out birth 19-55"   ///
>                                 "ruralOutBirth19to55High             c_rur_ob_19
> to55_h        census rural out birth 19-55 high"   ///
>                                 "urbanOutBirth19to55                   c_urb_ob_
> 19to55_         census urban out birth 19-55"   ///
>                                 "urbanOutBirth19to55High          c_urb_ob_19to5
> 5_h       census urban out birth 19-55 high"   ///
>                                 "ruralOutBirthA1                              c_
> rur_ob_A1                     census rural out birth A1"   ///
>                                 "urbanOutBirthA1                           c_urb
> _ob_A1                    census urban out birth A1"   ///
>                                 "ruralOutBirth19to55A1                          
>       c_rur_ob_19to55_A1     census rural out birth 19-55 A1"   ///
>                                 "ruralOutBirth19to55HighA1       c_rur_ob_19to55
> _h_A1                census rural out birth 19-55 high A1"  ///
>                                 "urbanOutBirth19to55A1              c_urb_ob_19t
> o55_A1    census urban out birth 19-55 A1"   ///
>                                 "urbanOutBirth19to55HighA1     c_urb_ob_19to55_h
> _A1               census urban out birth 19-55 high A1"  ///
>                                 "ruralOutBirthA2                              c_
> rur_ob_A2                     census rural out birth A2"   ///
>                                 "urbanOutBirthA2                           c_urb
> _ob_A2                    census urban out birth A2"   ///
>                                 "ruralOutBirth19to55A2                          
>       c_rur_ob_19to55_A2     census rural out birth 19-55 A2"   ///
>                                 "ruralOutBirth19to55HighA2       c_rur_ob_19to55
> _h_A2                census rural out birth 19-55 high A2"  ///
>                                 "urbanOutBirth19to55A2              c_urb_ob_19t
> o55_A2    census urban out birth 19-55 A2"   ///
>                                 "urbanOutBirth19to55HighA2     c_urb_ob_19to55_h
> _A2               census urban out birth 19-55 high A2"  {
  2.                 local  var_old = word("`list'",1)
  3.                 local  var_new = word("`list'",2)
  4.                 local  lab0 = trim(subinword("`list'","`var_old'","",1))
  5.                 local lab = trim(subinword("`lab0'","`var_new'","",1))
  6.                 rename `var_old' `var_new'
  7.                 label var `var_new' "`lab'"
  8.                 }

. 
. *** Set census variables to 0 in special districts
. foreach X of varlist c_* a1* a2* a3* a4* a7* a8* aN* {
  2. replace `X' = 0 if (unit_status==0 | unit_status==-1) & `X'==.
  3. }
(136 real changes made)
(136 real changes made)
(136 real changes made)
(136 real changes made)
(136 real changes made)
(136 real changes made)
(136 real changes made)
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. 
. ************** 7. Create Predicted GDP Variables **************
. 
. **** 1990 Rural County GDP
. *** Merge on CP Census data for urban units we need to impute
. sort city05 year

. merge city05 year using tempcen90.dta
(note: you are using old merge syntax; see [D] merge for new syntax)
variables city05 year do not uniquely identify observations in the master data

. tab _merge if year==1990

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      2,424       99.51       99.51
          3 |         12        0.49      100.00
------------+-----------------------------------
      Total |      2,436      100.00

. drop _merge

. *** Merge on city proper gdp data
. sort city05 year

. merge city05 year using ..\..\data\tabular_data_BJ\generated\cp90_gdp.dta
(note: you are using old merge syntax; see [D] merge for new syntax)
variables city05 year do not uniquely identify observations in the master data

. tab _merge year

           |                          year
    _merge |      1982       1990       1995       2000       2005 |     Total
-----------+-------------------------------------------------------+----------
         1 |     2,352        919      2,469      2,489      2,491 |    13,369 
         3 |         0      1,517          0          0          0 |     3,850 
-----------+-------------------------------------------------------+----------
     Total |     2,352      2,436      2,469      2,489      2,491 |    17,219 


           |         year
    _merge |      2008       2010 |     Total
-----------+----------------------+----------
         1 |     2,492        157 |    13,369 
         3 |         0      2,333 |     3,850 
-----------+----------------------+----------
     Total |     2,492      2,490 |    17,219 


. gen cgdp_sect1 = cgdp_py-cgdp_sect2-cgdp_sect3
(13,369 missing values generated)

. sort city05 year

. *** This is for urban units
. by city05 year: gen first = _n==1

. egen ctgdp_sect1 = sum(cgdp_sect1*first), by(province_code90 year)

. egen ctgdp_sect2 = sum(cgdp_sect2*first), by(province_code90 year)

. egen ctgdp_sect3 = sum(cgdp_sect3*first), by(province_code90 year)

. *** This is for county cities
. gen gdp_sect1 = gdp_py-gdp_sect2-gdp_sect3
(17,078 missing values generated)

. egen ccgdp_sect1 = sum(gdp_sect1), by(province_code90 year)

. egen ccgdp_sect2 = sum(gdp_sect2), by(province_code90 year)

. egen ccgdp_sect3 = sum(gdp_sect3), by(province_code90 year)

. *** These are GDP components for province remainders
. gen dgdp_sect1 = pgdp_sec1-ctgdp_sect1-ccgdp_sect1
(14,783 missing values generated)

. gen dgdp_sect2 = pgdp_sec2-ctgdp_sect2-ccgdp_sect2
(14,783 missing values generated)

. gen dgdp_sect3 = pgdp_sec3-ctgdp_sect3-ccgdp_sect3
(14,783 missing values generated)

. *** These are the units that we need to impute GDP for (including 3 prefec citie
> s with no py gdp data)
. gen impute = gdp_py==.

. replace impute = 0 if ((unit_status==1|unit_status==0) & _merge==3)
(1,439 real changes made)

. drop _merge

. 
. *** Do population based imputation
. gen emp_sector1 = a384+a390
(15,199 missing values generated)

. gen emp_sector2 = a387
(15,199 missing values generated)

. gen emp_sector3 = a381-emp_sector2-emp_sector1
(15,199 missing values generated)

. egen ctemp_sect1 = sum(cemp_sect1*first), by(province_code90 year)

. egen ctemp_sect2 = sum(cemp_sect2*first), by(province_code90 year)

. egen ctemp_sect3 = sum(cemp_sect3*first), by(province_code90 year)

. egen totemp_sect1 = sum(emp_sector1*impute), by(province_code90 year)

. replace totemp_sect1 = totemp_sect1+ctemp_sect1 if ctemp_sect1~=.
(245 real changes made)

. egen totemp_sect2 = sum(emp_sector2*impute), by(province_code90 year)

. replace totemp_sect2 = totemp_sect2+ctemp_sect2 if ctemp_sect2~=.
(245 real changes made)

. egen totemp_sect3 = sum(emp_sector3*impute), by(province_code90 year)

. replace totemp_sect3 = totemp_sect3+ctemp_sect3 if ctemp_sect3~=.
(245 real changes made)

. gen frac1 = emp_sector1/totemp_sect1
(15,335 missing values generated)

. replace frac1 = 0 if impute==0
(1,577 real changes made)

. gen frac2 = emp_sector2/totemp_sect2
(15,335 missing values generated)

. replace frac2 = 0 if impute==0
(1,577 real changes made)

. gen frac3 = emp_sector3/totemp_sect3
(15,329 missing values generated)

. replace frac3 = 0 if impute==0
(1,575 real changes made)

. gen gdp_predx1 = frac1*dgdp_sect1
(14,786 missing values generated)

. gen gdp_predx2 = frac2*dgdp_sect2
(14,786 missing values generated)

. gen gdp_predx3 = frac3*dgdp_sect3
(14,786 missing values generated)

. gen gdp_predc = gdp_predx1+gdp_predx2+gdp_predx3
(14,786 missing values generated)

. gen gdp_sect2_predc = gdp_predx2
(14,786 missing values generated)

. replace gdp_predc = . if impute==0
(704 real changes made, 704 to missing)

. replace gdp_sect2_predc = . if impute==0
(704 real changes made, 704 to missing)

. drop gdp_predx1 gdp_predx2 gdp_predx3

. 
. *** Fill in gdp_predc for all urban units in 1990 w/gdp numbers (to be used for 
> the 8 downgraded by 2010)
. gen empx_sector1 = c_emp_ag+c_emp_min
(7,496 missing values generated)

. gen empx_sector2 = c_emp_man
(7,496 missing values generated)

. gen empx_sector3 = c_emp-empx_sector2-empx_sector1
(7,496 missing values generated)

. egen totempx_sect1 = sum(empx_sector1*(cp90==1)*(unit_status==1)), by(city05 yea
> r)

. egen totempx_sect2 = sum(empx_sector2*(cp90==1)*(unit_status==1)), by(city05 yea
> r)

. egen totempx_sect3 = sum(empx_sector3*(cp90==1)*(unit_status==1)), by(city05 yea
> r)

. gen fracx1 = empx_sector1/totempx_sect1
(10,367 missing values generated)

. gen fracx2 = empx_sector2/totempx_sect2
(10,353 missing values generated)

. gen fracx3 = empx_sector3/totempx_sect3
(10,351 missing values generated)

. gen gdp_predx1 = fracx1*cgdp_sect1
(15,824 missing values generated)

. gen gdp_predx2 = fracx2*cgdp_sect2
(15,810 missing values generated)

. gen gdp_predx3 = fracx3*cgdp_sect3
(15,808 missing values generated)

. replace gdp_predc = gdp_predx1+gdp_predx2+gdp_predx3 if year==1990 & gdp_predc==
> . & unit_status==1
(509 real changes made)

. replace gdp_sect2_predc = gdp_predx2  if year==1990 & gdp_sect2_predc==. & unit_
> status==1
(513 real changes made)

. drop gdp_predx*

. 
. *** Fill in gdp_predc for all urban units in 1990 that need to be imputed (no 10
> 0% census count data)
. gen gdp_predx1 = fracx1*(cemp_sect1/totemp_sect1)*dgdp_sect1
(17,208 missing values generated)

. gen gdp_predx2 = fracx2*(cemp_sect2/totemp_sect2)*dgdp_sect2
(17,208 missing values generated)

. gen gdp_predx3 = fracx3*(cemp_sect3/totemp_sect3)*dgdp_sect3
(17,208 missing values generated)

. replace gdp_predc = gdp_predx1+gdp_predx2+gdp_predx3 if year==1990 & gdp_predc==
> . & unit_status==1
(2 real changes made)

. replace gdp_sect2_predc = gdp_predx2  if year==1990 & gdp_sect2_predc==. & unit_
> status==1
(2 real changes made)

. *** These are a few units we can't impute for, but have had their portions go ne
> arby
. replace gdp_predc = 0 if year==1990 & gdp_predc==. & unit_status==1 & c_totalPop
> ==.
(92 real changes made)

. replace gdp_sect2_predc = 0 if year==1990 & gdp_sect2_predc==. & unit_status==1 
> & c_totalPop==.
(92 real changes made)

. drop empx_sect* totempx_sect* fracx*

. 
. **** Perform similar exercise with the fenxian data
. ** For areas with no Fenxian data, do as above
. gen nofenxian = gdp_fenxian==.

. *** Impute a few missing sector breakdown fenxian obs
. replace gdp_sector1_fenxian = gdp_fenxian*emp_sector1/a381 if gdp_sector1_fenxia
> n==.
(0 real changes made)

. replace gdp_sector2_fenxian = gdp_fenxian*emp_sector2/a381 if gdp_sector2_fenxia
> n==.
(3 real changes made)

. replace gdp_sector3_fenxian = gdp_fenxian*emp_sector3/a381 if gdp_sector3_fenxia
> n==.
(3 real changes made)

. egen frac1nofenxian = sum(frac1*nofenxian), by(province_code90 year)

. egen frac2nofenxian = sum(frac2*nofenxian), by(province_code90 year)

. egen frac3nofenxian = sum(frac3*nofenxian), by(province_code90 year)

. egen totfen_sect1 = sum(gdp_sector1_fenxian*impute), by(province_code90 year)

. egen totfen_sect2 = sum(gdp_sector2_fenxian*impute), by(province_code90 year)

. egen totfen_sect3 = sum(gdp_sector3_fenxian*impute), by(province_code90 year)

. gen fracf1 = gdp_sector1_fenxian/totfen_sect1
(15,455 missing values generated)

. gen fracf2 = gdp_sector2_fenxian/totfen_sect2
(15,455 missing values generated)

. gen fracf3 = gdp_sector3_fenxian/totfen_sect3
(15,455 missing values generated)

. gen gdp_predy1 = fracf1*(1-frac1nofenxian)*impute*dgdp_sect1
(15,455 missing values generated)

. gen gdp_predy2 = fracf2*(1-frac2nofenxian)*impute*dgdp_sect2
(15,455 missing values generated)

. gen gdp_predy3 = fracf3*(1-frac3nofenxian)*impute*dgdp_sect3
(15,455 missing values generated)

. gen gdp_predf = gdp_predy1+gdp_predy2+gdp_predy3
(15,455 missing values generated)

. replace gdp_predf = gdp_predc if gdp_predf==.
(654 real changes made)

. gen gdp_sect2_predf = gdp_predy2
(15,455 missing values generated)

. replace gdp_sect2_predf = gdp_sect2_predc if gdp_sect2_predf==.
(658 real changes made)

. replace gdp_predf = . if impute==0
(686 real changes made, 686 to missing)

. replace gdp_sect2_predf = . if impute==0
(690 real changes made, 690 to missing)

. replace gdp_predf = gdp_predc if year==1990 & gdp_predf==. & unit_status==1
(600 real changes made)

. replace gdp_sect2_predf = gdp_sect2_predc  if year==1990 & gdp_sect2_predf==. & 
> unit_status==1
(604 real changes made)

. drop dgdp_* ccgdp_* ctgdp_* province_code90 impute emp_sect*

. drop frac1 frac2 frac3 ctemp_sect* frac1n* totfen_* first gdp_predy* gdp_predx*

. 
. *** Create predicted rural GDP in 2000, 2005 & 2010 for a few obs
. reg gdp_michigan c_totalPop c_emp-c_emp_tran if year==2000 & (unit_status==2|uni
> t_status==3)
note: c_emp_man_chem omitted because of collinearity
note: c_emp_cul omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =     1,712
-------------+----------------------------------   F(16, 1695)     =    594.28
       Model |  1876895.49        16  117305.968   Prob > F        =    0.0000
    Residual |  334576.748     1,695  197.390412   R-squared       =    0.8487
-------------+----------------------------------   Adj R-squared   =    0.8473
       Total |  2211472.24     1,711  1292.50277   Root MSE        =     14.05

---------------------------------------------------------------------------------
   gdp_michigan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
     c_totalPop |   .0031005   .0007335     4.23   0.000     .0016618    .0045392
          c_emp |  -.1843916   .0876963    -2.10   0.036     -.356396   -.0123871
       c_emp_ag |   .1822927   .0877216     2.08   0.038     .0102387    .3543467
      c_emp_min |   .1934025   .0876732     2.21   0.028     .0214434    .3653615
      c_emp_man |   .2738365   .0895983     3.06   0.002     .0981016    .4495714
 c_emp_man_trad |   -.072066   .0181199    -3.98   0.000    -.1076058   -.0365263
 c_emp_man_chem |          0  (omitted)
  c_emp_man_hvy |  -.0394998   .0202068    -1.95   0.051    -.0791328    .0001332
c_emp_man_hit~h |  -.1874474     .02059    -9.10   0.000    -.2278318    -.147063
     c_emp_util |   .1897633    .089243     2.13   0.034     .0147252    .3648013
     c_emp_cons |   .2165817    .087787     2.47   0.014     .0443994     .388764
     c_emp_soft |   .0911701   .1549379     0.59   0.556    -.2127196    .3950599
     c_emp_whsl |   .2293152   .0897861     2.55   0.011     .0532119    .4054184
     c_emp_fina |   .7246774    .101493     7.14   0.000     .5256126    .9237422
      c_emp_res |   .1866914   .0905358     2.06   0.039     .0091177    .3642652
      c_emp_cul |          0  (omitted)
      c_emp_pub |   .1891002   .0878434     2.15   0.031     .0168072    .3613932
     c_emp_tran |   .0752815   .0298142     2.53   0.012     .0168049    .1337581
          _cons |  -2.807863   .6886779    -4.08   0.000    -4.158611   -1.457115
---------------------------------------------------------------------------------

. predict gdp_predx if year==2000
(option xb assumed; fitted values)
(14,730 missing values generated)

. replace gdp_michigan = gdp_predx if year==2000 & gdp_michigan==. & (unit_status=
> =2|unit_status==3)
(6 real changes made)

. reg gdp_michigan c_totalPop c_emp-c_emp_tran if year==2005 & (unit_status==2|uni
> t_status==3)

      Source |       SS           df       MS      Number of obs   =     1,652
-------------+----------------------------------   F(18, 1633)     =    559.51
       Model |  10092396.6        18  560688.698   Prob > F        =    0.0000
    Residual |  1636451.91     1,633  1002.11385   R-squared       =    0.8605
-------------+----------------------------------   Adj R-squared   =    0.8589
       Total |  11728848.5     1,651  7104.08751   Root MSE        =    31.656

---------------------------------------------------------------------------------
   gdp_michigan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
     c_totalPop |  -.0272803   .0057562    -4.74   0.000    -.0385705     -.01599
          c_emp |  -2.025082   .6026305    -3.36   0.001    -3.207092   -.8430719
       c_emp_ag |   2.097946   .6018114     3.49   0.001      .917542    3.278349
      c_emp_min |   2.359246   .6082197     3.88   0.000     1.166273    3.552219
      c_emp_man |   2.436739   1.441327     1.69   0.091     -.390304    5.263783
 c_emp_man_trad |  -.1213009   1.173426    -0.10   0.918     -2.42288    2.180279
 c_emp_man_chem |  -.0878374   1.176409    -0.07   0.940    -2.395267    2.219592
  c_emp_man_hvy |   .1998885   1.174435     0.17   0.865    -2.103669    2.503446
c_emp_man_hit~h |  -.4664966   1.170894    -0.40   0.690    -2.763109    1.830116
     c_emp_util |   2.217852   .6029902     3.68   0.000     1.035136    3.400568
     c_emp_cons |   2.494073   .6057224     4.12   0.000     1.305999    3.682148
     c_emp_soft |   4.141395   .7270565     5.70   0.000     2.715333    5.567456
     c_emp_whsl |   2.120642   .6088111     3.48   0.001     .9265091    3.314775
     c_emp_fina |   3.628353   .6499599     5.58   0.000      2.35351    4.903196
      c_emp_res |   2.233182   .6097755     3.66   0.000     1.037158    3.429207
      c_emp_cul |   2.525291   .7988037     3.16   0.002     .9585034    4.092079
      c_emp_pub |   1.984143    .617043     3.22   0.001     .7738642    3.194422
     c_emp_tran |   1.380247   .2395556     5.76   0.000     .9103781    1.850115
          _cons |   10.59408   2.542489     4.17   0.000     5.607196    15.58096
---------------------------------------------------------------------------------

. predict gdp_predy if year==2005
(option xb assumed; fitted values)
(14,728 missing values generated)

. replace gdp_michigan = gdp_predy if year==2005 & gdp_michigan==. & (unit_status=
> =2|unit_status==3)
(4 real changes made)

. reg gdp_michigan a102 aN6003 aN6005-aN6014 aN6016-aN6021 if year==2010 & (unit_s
> tatus==2|unit_status==3)

      Source |       SS           df       MS      Number of obs   =     1,569
-------------+----------------------------------   F(18, 1550)     =    488.51
       Model |  45626454.4        18  2534803.02   Prob > F        =    0.0000
    Residual |  8042729.42     1,550  5188.85769   R-squared       =    0.8501
-------------+----------------------------------   Adj R-squared   =    0.8484
       Total |  53669183.8     1,568  34227.7958   Root MSE        =    72.034

------------------------------------------------------------------------------
gdp_michigan |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        a102 |   .0001003   .0000308     3.26   0.001     .0000399    .0001607
      aN6003 |  -.0000722   .0004719    -0.15   0.878    -.0009977    .0008534
      aN6005 |   .0029496   .0005363     5.50   0.000     .0018976    .0040017
      aN6006 |   .2494379   .0266328     9.37   0.000     .1971977     .301678
      aN6007 |  -.0013261   .0018949    -0.70   0.484    -.0050428    .0023907
      aN6008 |   .0339628    .006686     5.08   0.000     .0208482    .0470773
      aN6009 |  -.2331028    .054768    -4.26   0.000      -.34053   -.1256755
      aN6010 |  -.0000521   .0028054    -0.02   0.985     -.005555    .0054508
      aN6011 |  -.0436052   .0064914    -6.72   0.000     -.056338   -.0308723
      aN6012 |   .4142824   .0513833     8.06   0.000     .3134944    .5150705
      aN6013 |   .1440562   .0306478     4.70   0.000     .0839407    .2041717
      aN6014 |   .1175877   .0209118     5.62   0.000     .0765692    .1586062
      aN6016 |   .3021515   .0349933     8.63   0.000     .2335123    .3707907
      aN6017 |   .0201966   .0074335     2.72   0.007     .0056157    .0347774
      aN6018 |  -.0599021   .0185676    -3.23   0.001    -.0963223   -.0234818
      aN6019 |   .0210897   .0361634     0.58   0.560    -.0498447    .0920241
      aN6020 |   .0052376   .0430303     0.12   0.903    -.0791662    .0896414
      aN6021 |   .0025177   .0072621     0.35   0.729    -.0117268    .0167623
       _cons |   -18.5587   4.220726    -4.40   0.000    -26.83764   -10.27977
------------------------------------------------------------------------------

. predict gdp_predz if year==2010
(option xb assumed; fitted values)
(14,820 missing values generated)

. replace gdp_michigan = gdp_predz if year==2010 & gdp_michigan==. & (unit_status=
> =2|unit_status==3)
(5 real changes made)

. drop gdp_predx gdp_predy gdp_predz

. drop nofenxian frac2nofenxian frac3nofenxian fracf1 fracf2 fracf3 pgdp_*

. 
. *** Predict Net Assets in 1990 (very crude) for rural cp10=1 units with core cov
> ered
. gen lasset_n_qz=log(asset_n_qz)
(15,726 missing values generated)

. gen la387 = log(a387)
(15,339 missing values generated)

. reg lasset_n_qz la387 if year==1990 & (unit_status==2|unit_status==3)

      Source |       SS           df       MS      Number of obs   =        81
-------------+----------------------------------   F(1, 79)        =     46.12
       Model |  22.5845676         1  22.5845676   Prob > F        =    0.0000
    Residual |  38.6829303        79  .489657346   R-squared       =    0.3686
-------------+----------------------------------   Adj R-squared   =    0.3606
       Total |  61.2674979        80  .765843724   Root MSE        =    .69976

------------------------------------------------------------------------------
 lasset_n_qz |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       la387 |   .7409653   .1091034     6.79   0.000     .5238005    .9581301
       _cons |   2.854778   1.159341     2.46   0.016     .5471663    5.162389
------------------------------------------------------------------------------

. predict lassetn if year==1990 & cp10==1 & (unit_status==2|unit_status==3)
(option xb assumed; fitted values)
(17,004 missing values generated)

. gen dasset = asset_n_qz~=.

. replace dasset = 1 if unit_status==1
(4,891 real changes made)

. egen okcp = max(dasset) if cp10==1, by(city05 year)
(11278 missing values generated)

. replace asset_n_qz = exp(lassetn+(e(rmse)^2)/2) if cp10==1 & okcp==1 & (unit_sta
> tus==2|unit_status==3) & year==1990
(171 real changes made)

. drop dasset lassetn la387 okcp

. 
. *** This is so it can be combined with the prefecture level data
. drop culland

. rename cularea culland

. 
. *** Predict Sector 2 GDP in 2010 by allocating reported numbers using manufactur
> ing employment shares
. sort city05 year

. merge city05 year using ../../data/tabular_data_BJ/generated/pf10_gdp.dta
(note: you are using old merge syntax; see [D] merge for new syntax)
variables city05 year do not uniquely identify observations in the master data

. tab _merge year

           |                          year
    _merge |      1982       1990       1995       2000       2005 |     Total
-----------+-------------------------------------------------------+----------
         1 |     2,352      2,436      2,469      2,489      2,491 |    14,884 
         3 |         0          0          0          0          0 |     2,335 
-----------+-------------------------------------------------------+----------
     Total |     2,352      2,436      2,469      2,489      2,491 |    17,219 


           |         year
    _merge |      2008       2010 |     Total
-----------+----------------------+----------
         1 |     2,492        155 |    14,884 
         3 |         0      2,335 |     2,335 
-----------+----------------------+----------
     Total |     2,492      2,490 |    17,219 


. gen city = year==2010 & (unit_status==1|unit_status==0)

. drop cemp_sect2

. egen cemp = sum(a381*city), by(city05 year)

. egen pfemp = sum(a381), by(city year)

. egen cemp_sect2 = sum(aN6005*city), by(city05 year)

. egen pfemp_sect2 = sum(aN6005), by(city05 year)

. replace gdp_predc = cgdp_py*a381/cemp if city==1
(831 real changes made)

. replace gdp_predc = (pgdp_py-cgdp_py)*a381/(pfemp-cemp) if city==0 & year==2010
(1,502 real changes made)

. replace gdp_sect2_predc = cgdp_sect2*aN6005/cemp_sect2 if city==1
(831 real changes made)

. replace gdp_sect2_predc = (pgdp_sect2-cgdp_sect2)*aN6005/(pfemp_sect2-cemp_sect2
> ) if city==0 & year==2010
(1,502 real changes made)

. drop city cemp pfemp cgdp_py pgdp_py cemp_sect2 pfemp_sect2 pgdp_* cgdp_* _merge

. 
. 
. ***************** 8. Label Variables ***************************
. 
. label variable a101 "Total no. of H                                            T
> able 2-19"

. label variable a102 "Total P                                                    
>         "

. label variable a103 "Total M                                   H=household(s)   
>         "

. label variable a104 "Total F                                   M=male(s)        
>         "

. label variable a105 "Residents of urban wards: No. of H        F=female(s)      
>         "

. label variable a106 "                     : P               P=population (M+F)  
>      "

. label variable a107 "                     : M                                   
>      "

. label variable a108 "                     : F                                   
>      "

. label variable a109 "Residents of villages: No. of H                            
>         "

. label variable a110 "                  : P                                      
>      "

. label variable a111 "                  : M                                      
>      "

. label variable a112 "                  : F                                      
>      "

. label variable a113 "Residents of rural units outside village jurisdiction: No. 
> of H    "

. label variable a114 "                                                  : P      
>      "

. label variable a115 "                                                  : M      
>      "

. label variable a116 "                                                  : F      
>      "

. label variable a117 "Non-agricultural H: P                                      
>         "

. label variable a118 "               : M                                         
>      "

. label variable a119 "               : F                                         
>      "

. label variable a120 "Agricultural H: P                                          
>         "

. label variable a121 "           : M                                             
>      "

. label variable a122 "           : F                                             
>      "

. label variable a123 "Specially designated H and persons outside any H: P        
>         "

. label variable a124 "                                             : M           
>      "

. label variable a125 "                                             : F           
>      "

. label variable a126 "All residents of all zhen (townships) combined: No. of H  T
> able 2-11"

. label variable a127 "                                       : P                 
>  "

. label variable a128 "                                       : M                 
>  "

. label variable a129 "                                       : F                 
>  "

. label variable a130 "Town residents in all zhen combined: No. of H              
>         "

. label variable a131 "                                 : P                       
>       "

. label variable a132 "                                 : M                       
>       "

. label variable a133 "                                 : F                       
>       "

. label variable a134 "Rural residents in all zhen combined: No. of H             
>         "

. label variable a135 "                                  : P                      
>       "

. label variable a136 "                                  : M                      
>       "

. label variable a137 "                                  : F                      
>       "

. label variable a138 "Non-agricultural H in all zhen combined: P                T
> able 2-12"

. label variable a139 "                                     : M                   
>       "

. label variable a140 "                                     : F                   
>       "

. label variable a141 "Agricultural H in all zhen combined: P                     
>         "

. label variable a142 "                              : M                          
>    "

. label variable a143 "                           : F                             
>    "

. label variable a144 "Specially designated H, etc. in all zhen combined: P       
>         "

. label variable a145 "                                            : M            
>    "

. label variable a146 "                                            : F            
>    "

. label variable a181 "Total P, all ages and both sexes                           
>          A1"

. label variable a182 "Total M, all ages                                          
>          A2"

. label variable a183 "Total F, all ages                                          
>          A3"

. label variable a184 "P 0-4                                                      
>          A4"

. label variable a185 "M 0-4                                                      
>          A5"

. label variable a186 "F 0-4                                                      
>          A6"

. label variable a187 "P 5-9                                                      
>          A7"

. label variable a188 "M 5-9                                                      
>          A8"

. label variable a189 "F 5-9                                                      
>          A9"

. label variable a190 "P 10-14                                                    
>         A10"

. label variable a191 "M 10-14                                                    
>         A11"

. label variable a192 "F 10-14                                                    
>         A12"

. label variable a193 "P 15-19                                                    
>         A13"

. label variable a194 "M 15-19                                                    
>         A14"

. label variable a195 "F 15-19                                                    
>         A15"

. label variable a196 "P 20-24                                                    
>         A16"

. label variable a197 "M 20-24                                                    
>         A17"

. label variable a198 "F 20-24                                                    
>         A18"

. label variable a199 "P 25-29                                                    
>         A19"

. label variable a200 "M 25-29                                                    
>         A20"

. label variable a201 "F 25-29                                                    
>         A21"

. label variable a202 "P 30-34                                                    
>         A22"

. label variable a203 "M 30-34                                                    
>         A23"

. label variable a204 "F 30-34                                                    
>         A24"

. label variable a205 "P 35-39                                                    
>         A25"

. label variable a206 "M 35-39                                                    
>         A26"

. label variable a207 "F 35-39                                                    
>         A27"

. label variable a208 "P 40-44                                                    
>         A28"

. label variable a209 "M 40-44                                                    
>         A29"

. label variable a210 "F 40-44                                                    
>         A30"

. label variable a211 "P 45-49                                                    
>         A31"

. label variable a212 "M 45-49                                                    
>         A32"

. label variable a213 "F 45-49                                                    
>         A33"

. label variable a214 "P 50-54                                                    
>         A34"

. label variable a215 "M 50-54                                                    
>         A35"

. label variable a216 "F 50-54                                                    
>         A36"

. label variable a217 "P 55-59                                                    
>         A37"

. label variable a218 "M 55-59                                                    
>         A38"

. label variable a219 "F 55-59                                                    
>         A39"

. label variable a220 "P 60-64                                                    
>         A40"

. label variable a221 "M 60-64                                                    
>         A41"

. label variable a222 "F 60-64                                                    
>         A42"

. label variable a223 "P 65-69                                                    
>         A43"

. label variable a224 "M 65-69                                                    
>         A44"

. label variable a225 "F 65-69                                                    
>         A45"

. label variable a226 "P 70-74                                                    
>         A46"

. label variable a227 "M 70-74                                                    
>         A47"

. label variable a228 "F 70-74                                                    
>         A48"

. label variable a229 "P 75-79                                                    
>         A49"

. label variable a230 "M 75-79                                                    
>         A50"

. label variable a231 "F 75-79                                                    
>         A51"

. label variable a232 "P 80-84                                                    
>         A52"

. label variable a233 "M 80-84                                                    
>         A53"

. label variable a234 "F 80-84                                                    
>         A54"

. label variable a235 "P 85-89                                                    
>         A55"

. label variable a236 "M 85-89                                                    
>         A56"

. label variable a237 "F 85-89                                                    
>         A57"

. label variable a238 "P 90-94                                                    
>         A58"

. label variable a239 "M 90-94                                                    
>         A59"

. label variable a240 "F 90-94                                                    
>         A60"

. label variable a241 "P 95-99                                                    
>         A61"

. label variable a242 "M 95-99                                                    
>         A62"

. label variable a243 "F 95-99                                                    
>         A63"

. label variable a244 "P 100+                                                     
>         A64"

. label variable a245 "M 100+                                                     
>         A65"

. label variable a246 "F 100+                                                     
>         A66"

. label variable a261 "Total P aged 6+                                            
>          E1"

. label variable a262 "Total M aged 6+                                            
>          E2"

. label variable a263 "Total F aged 6+                                            
>          E3"

. label variable a264 "P university                                               
>          E4"

. label variable a265 "M                                                          
>          E5"

. label variable a266 "F                                                          
>          E6"

. label variable a267 "P technical/junior college                                 
>          E7"

. label variable a268 "M                                                          
>          E8"

. label variable a269 "F                                                          
>          E9"

. label variable a270 "P secondary technical school                               
>         E10"

. label variable a271 "M                                                          
>         E11"

. label variable a272 "F                                                          
>         E12"

. label variable a273 "P senior middle school                                     
>         E13"

. label variable a274 "M                                                          
>         E14"

. label variable a275 "F                                                          
>         E15"

. label variable a276 "P junior middle school                                     
>         E16"

. label variable a277 "M                                                          
>         E17"

. label variable a278 "F                                                          
>         E18"

. label variable a279 "P primary school                                           
>         E19"

. label variable a280 "M                                                          
>         E20"

. label variable a281 "F                                                          
>         E21"

. label variable a282 "P illiterate/semi-illiterate                               
>         E22"

. label variable a283 "M                                                          
>         E23"

. label variable a284 "F                                                          
>         E24"

. label variable a291 "Total P 15+                                                
>          E1"

. label variable a292 "Total M 15+                                                
>          E2"

. label variable a293 "Total F 15+                                                
>          E3"

. label variable a294 "P illiterate/semi-illiterate                               
>          E4"

. label variable a295 "M                                                          
>          E5"

. label variable a296 "F                                                          
>          E6"

. label variable a301 "Total P 15+                                                
>          M1"

. label variable a302 "Total M 15+                                                
>          M2"

. label variable a303 "Total F 15+                                                
>          M3"

. label variable a304 "P never married                                            
>          M4"

. label variable a305 "M                                                          
>          M5"

. label variable a306 "F                                                          
>          M6"

. label variable a307 "P married                                                  
>          M7"

. label variable a308 "M                                                          
>          M8"

. label variable a309 "F                                                          
>          M9"

. label variable a310 "P widowed                                                  
>         M10"

. label variable a311 "M                                                          
>         M11"

. label variable a312 "F                                                          
>         M12"

. label variable a313 "P divorced                                                 
>         M13"

. label variable a314 "M                                                          
>         M14"

. label variable a315 "F                                                          
>         M15"

. label variable a321 "All births 1Jan89-30Jun90                                  
>          B1"

. label variable a322 "M births                                                   
>          B2"

. label variable a323 "F births                                                   
>          B3"

. label variable a324 "All births 1Jan89-30Jun89                                  
>          B4"

. label variable a325 "M births                                                   
>          B5"

. label variable a326 "F births                                                   
>          B6"

. label variable a327 "All births 1Jul89-31Dec89                                  
>          B7"

. label variable a328 "M births                                                   
>          B8"

. label variable a329 "F births                                                   
>          B9"

. label variable a330 "All births 1Jan90-30Jun90                                  
>         B10"

. label variable a331 "M births                                                   
>         B11"

. label variable a332 "F births                                                   
>         B12"

. label variable a341 "All deaths 1Jan89-30Jun90                                  
>          D1"

. label variable a342 "M deaths                                                   
>          D2"

. label variable a343 "F deaths                                                   
>          D3"

. label variable a344 "All deaths 1Jan89-30Jun89                                  
>          D4"

. label variable a345 "M deaths                                                   
>          D5"

. label variable a346 "F deaths                                                   
>          D6"

. label variable a347 "All deaths 1Jul89-31Dec89                                  
>          D7"

. label variable a348 "M deaths                                                   
>          D8"

. label variable a349 "F deaths                                                   
>          D9"

. label variable a350 "All deaths 1Jan90-30Jun90                                  
>         D10"

. label variable a351 "M deaths                                                   
>         D11"

. label variable a352 "F deaths                                                   
>         D12"

. label variable a361 "Total inmigrants                                           
>          R1"

. label variable a362 "Within-province inmigrants: Total                          
>          R2"

. label variable a363 "                       : from municipal cities             
>       R3"

. label variable a364 "                       : from zhen (urban townships)       
>       R4"

. label variable a365 "                       : from xiang (rural townships)      
>       R5"

. label variable a366 "Inmigrants from other provinces: Total                     
>          R6"

. label variable a367 "                         : from municipal cities           
>       R7"

. label variable a368 "                         : from zhen                       
>       R8"

. label variable a369 "                         : from xiang                      
>       R9"

. label variable a370 "Other inmigrants                                           
>         R10"

. label variable a381 "Total employed P                                           
>          I1"

. label variable a382 "Total          M                                           
>          I2"

. label variable a383 "Total          F                                           
>          I3"

. label variable a384 "P Agric./forestry/animal husb./fishery/water conservancy   
>          I4"

. label variable a385 "M                                                          
>          I5"

. label variable a386 "F                                                          
>          I6"

. label variable a387 "P Industry                                                 
>          I7"

. label variable a388 "M                                                          
>          I8"

. label variable a389 "F                                                          
>          I9"

. label variable a390 "P Mining, prospecting                                      
>         I10"

. label variable a391 "M                                                          
>         I11"

. label variable a392 "F                                                          
>         I12"

. label variable a393 "P Construction                                             
>         I13"

. label variable a394 "M                                                          
>         I14"

. label variable a395 "F                                                          
>         I15"

. label variable a396 "P Transport, posts, telecommunications                     
>         I16"

. label variable a397 "M                                                          
>         I17"

. label variable a398 "F                                                          
>         I18"

. label variable a399 "P Commerce, supply and marketing                           
>         I19"

. label variable a400 "M                                                          
>         I20"

. label variable a401 "F                                                          
>         I21"

. label variable a402 "P Real estate, utilities, residential services             
>         I22"

. label variable a403 "M                                                          
>         I23"

. label variable a404 "F                                                          
>         I24"

. label variable a405 "P Medicine, health care, sports, welfare                   
>         I25"

. label variable a406 "M                                                          
>         I26"

. label variable a407 "F                                                          
>         I27"

. label variable a408 "P Education, culture, arts, radio, television              
>         I28"

. label variable a409 "M                                                          
>         I29"

. label variable a410 "F                                                          
>         I30"

. label variable a411 "P Science, technology                                      
>         I31"

. label variable a412 "M                                                          
>         I32"

. label variable a413 "F                                                          
>         I33"

. label variable a414 "P Finance, insurance                                       
>         I34"

. label variable a415 "M                                                          
>         I35"

. label variable a416 "F                                                          
>         I36"

. label variable a417 "P Government, party, and NGOs                              
>         I37"

. label variable a418 "M                                                          
>         I38"

. label variable a419 "F                                                          
>         I39"

. label variable a420 "P Other economic activities                                
>         I40"

. label variable a421 "M                                                          
>         I41"

. label variable a422 "F                                                          
>         I42"

. label variable a431 "P Professional and high-level technical personnel          
>          O1"

. label variable a432 "M                                                          
>          O2"

. label variable a433 "F                                                          
>          O3"

. label variable a434 "P Officials/managers in gov't, party, business, & NGOs     
>          O4"

. label variable a435 "M                                                          
>          O5"

. label variable a436 "F                                                          
>          O6"

. label variable a437 "P Clerical personnel                                       
>          O7"

. label variable a438 "M                                                          
>          O8"

. label variable a439 "F                                                          
>          O9"

. label variable a440 "P Employees in commercial sector                           
>         O10"

. label variable a441 "M                                                          
>         O11"

. label variable a442 "F                                                          
>         O12"

. label variable a443 "P Employees in service sector                              
>         O13"

. label variable a444 "M                                                          
>         O14"

. label variable a445 "F                                                          
>         O15"

. label variable a446 "P Workers in agric., forestry, animal husb., fisheries     
>         O16"

. label variable a447 "M                                                          
>         O17"

. label variable a448 "F                                                          
>         O18"

. label variable a449 "P Workers in manufacturing, construction, transport, etc.  
>         O19"

. label variable a450 "M                                                          
>         O20"

. label variable a451 "F                                                          
>         O21"

. label variable a452 "P Other and misc. occupations                              
>         O22"

. label variable a453 "M                                                          
>         O23"

. label variable a454 "F                                                          
>         024"

. label variable a701 "P Han Chinese"

. label variable a702 "M        "

. label variable a703 "F        "

. label variable a704 "P Mongol (Menggu) minority"

. label variable a705 "M        "

. label variable a706 "F        "

. label variable a707 "P Hui minority"

. label variable a708 "M        "

. label variable a709 "F        "

. label variable a710 "P Tibetan (Zang) minority"

. label variable a711 "M        "

. label variable a712 "F        "

. label variable a713 "P Uygur (Weiwu`er) minority"

. label variable a714 "M        "

. label variable a715 "F        "

. label variable a716 "P Miao minority"

. label variable a717 "M        "

. label variable a718 "F        "

. label variable a719 "P Yi minority"

. label variable a720 "M        "

. label variable a721 "F        "

. label variable a722 "P Zhuang minority"

. label variable a723 "M        "

. label variable a724 "F        "

. label variable a725 "P Bouyei (Buyi) minority"

. label variable a726 "M        "

. label variable a727 "F        "

. label variable a728 "P Korean (Chaoxian) minority"

. label variable a729 "M        "

. label variable a730 "F        "

. label variable a731 "P Manchu (Man) minority"

. label variable a732 "M        "

. label variable a733 "F        "

. label variable a734 "P Dong minority"

. label variable a735 "M        "

. label variable a736 "F        "

. label variable a737 "P Yao minority"

. label variable a738 "M        "

. label variable a739 "F        "

. label variable a740 "P Bai minority"

. label variable a741 "M        "

. label variable a742 "F        "

. label variable a743 "P Tujia minority"

. label variable a744 "M        "

. label variable a745 "F        "

. label variable a746 "P Hani minority"

. label variable a747 "M        "

. label variable a748 "F        "

. label variable a749 "P Kazak (Hasake) minority"

. label variable a750 "M        "

. label variable a751 "F        "

. label variable a752 "P Dai minority"

. label variable a753 "M        "

. label variable a754 "F        "

. label variable a755 "P Li minority"

. label variable a756 "M        "

. label variable a757 "F        "

. label variable a758 "P Lisu minority"

. label variable a759 "M        "

. label variable a760 "F        "

. label variable a761 "P Va (Wa) minority"

. label variable a762 "M        "

. label variable a763 "F        "

. label variable a764 "P She minority"

. label variable a765 "M        "

. label variable a766 "F        "

. label variable a767 "P Gaoshan minority"

. label variable a768 "M        "

. label variable a769 "F        "

. label variable a770 "P Lahu minority"

. label variable a771 "M        "

. label variable a772 "F        "

. label variable a773 "P Shui minority"

. label variable a774 "M        "

. label variable a775 "F        "

. label variable a776 "P Dongxiang minority"

. label variable a777 "M        "

. label variable a778 "F        "

. label variable a779 "P Naxi minority"

. label variable a780 "M        "

. label variable a781 "F        "

. label variable a782 "P Jingpo minority"

. label variable a783 "M        "

. label variable a784 "F        "

. label variable a785 "P Kirgiz (Ke`erkezi) minority"

. label variable a786 "M        "

. label variable a787 "F        "

. label variable a788 "P Tu minority"

. label variable a789 "M        "

. label variable a790 "F        "

. label variable a791 "P Daur (Dawo`er) minority"

. label variable a792 "M        "

. label variable a793 "F        "

. label variable a794 "P Mulam (Mulao) minority"

. label variable a795 "M        "

. label variable a796 "F        "

. label variable a797 "P Qiang minority"

. label variable a798 "M        "

. label variable a799 "F        "

. label variable a800 "P Blang (Bulang) minority"

. label variable a801 "M        "

. label variable a802 "F        "

. label variable a803 "P Salar (Sala) minority"

. label variable a804 "M        "

. label variable a805 "F        "

. label variable a806 "P Maonan minority"

. label variable a807 "M        "

. label variable a808 "F        "

. label variable a809 "P Gelo (Gelao) minority"

. label variable a810 "M        "

. label variable a811 "F        "

. label variable a812 "P Xibe (Xibo) minority"

. label variable a813 "M        "

. label variable a814 "F        "

. label variable a815 "P Achang minority"

. label variable a816 "M        "

. label variable a817 "F        "

. label variable a818 "P Pumi minority"

. label variable a819 "M        "

. label variable a820 "F        "

. label variable a821 "P Tajik (Tajike) minority"

. label variable a822 "M        "

. label variable a823 "F        "

. label variable a824 "P Nu minority"

. label variable a825 "M        "

. label variable a826 "F        "

. label variable a827 "P Uzbek (Wuzibieke) minority"

. label variable a828 "M        "

. label variable a829 "F        "

. label variable a830 "P Russian (Eluosi) minority"

. label variable a831 "M        "

. label variable a832 "F        "

. label variable a833 "P Ewenki (Ewenke) minority"

. label variable a834 "M        "

. label variable a835 "F        "

. label variable a836 "P De'ang minority"

. label variable a837 "M        "

. label variable a838 "F        "

. label variable a839 "P Bonan (Bao`an) minority"

. label variable a840 "M        "

. label variable a841 "F        "

. label variable a842 "P Yugur (Yugu) minority"

. label variable a843 "M        "

. label variable a844 "F        "

. label variable a845 "P Jing minority"

. label variable a846 "M        "

. label variable a847 "F        "

. label variable a848 "P Tatar (Tata`er) minority"

. label variable a849 "M        "

. label variable a850 "F        "

. label variable a851 "P Drung (Dulong) minority"

. label variable a852 "M        "

. label variable a853 "F        "

. label variable a854 "P Oroqen (Elunchun) minority"

. label variable a855 "M        "

. label variable a856 "F        "

. label variable a857 "P Hezhen (Hezhe) minority"

. label variable a858 "M        "

. label variable a859 "F        "

. label variable a860 "P Monba (Menba) minority"

. label variable a861 "M        "

. label variable a862 "F        "

. label variable a863 "P Lhopa (Luoba) minority"

. label variable a864 "M        "

. label variable a865 "F        "

. label variable a866 "P Jinuo minority"

. label variable a867 "M        "

. label variable a868 "F        "

. label variable a869 "P nationality/ethnicity unknown"

. label variable a870 "M                                     "

. label variable a871 "F                                     "

. label variable a872 "P naturalized citizens"

. label variable a873 "M                       "

. label variable a874 "F                       "

. label variable census_code "original census code (Loren's Data"

. label variable gbcenmq "original census code, 1990 aggregate data"

. label variable gdp_predc "predicted gdp for rural counties, census pop allocatio
> n only"

. label variable gdp_predf "predicted gdp for rural counties, fenxian gdp allocati
> on"

. 
. 
. ************* 9. Scale up Census Variables Using Pop Weights *******************
> *********
. 
. *** Scale up census variables
. gen sampop = c_totalPop
(7,496 missing values generated)

. foreach X of varlist c_* {
  2. replace `X' = `X'*100 if year==1982
  3. 
. replace `X' = `X'/0.0104856948259734 if province_code==110000 & year==1990
  4. replace `X' = `X'/0.0130791966036386 if province_code==120000 & year==1990
  5. replace `X' = `X'/0.00976147989113532 if province_code==130000 & year==1990
  6. replace `X' = `X'/0.0104715690183259 if province_code==140000 & year==1990
  7. replace `X' = `X'/0.0118723679087625 if province_code==150000 & year==1990
  8. replace `X' = `X'/0.0107576598978953 if province_code==210000 & year==1990
  9. replace `X' = `X'/0.010814713382742 if province_code==220000 & year==1990
 10. replace `X' = `X'/0.0110158000569816 if province_code==230000 & year==1990
 11. replace `X' = `X'/0.0115695700221318 if province_code==310000 & year==1990
 12. replace `X' = `X'/0.0103751284793056 if province_code==320000 & year==1990
 13. replace `X' = `X'/0.0104590486930804 if province_code==330000 & year==1990
 14. replace `X' = `X'/0.0120020335056383 if province_code==340000 & year==1990
 15. replace `X' = `X'/0.0114160505034443 if province_code==350000 & year==1990
 16. replace `X' = `X'/0.011929876629665 if province_code==360000 & year==1990
 17. replace `X' = `X'/0.00980405597741145 if province_code==370000 & year==1990
 18. replace `X' = `X'/0.0101516748980099 if province_code==410000 & year==1990
 19. replace `X' = `X'/0.0104176992770507 if province_code==420000 & year==1990
 20. replace `X' = `X'/0.0103084163513093 if province_code==430000 & year==1990
 21. replace `X' = `X'/0.0100002807610139 if province_code==440000 & year==1990
 22. replace `X' = `X'/0.010567331423635 if province_code==450000 & year==1990
 23. replace `X' = `X'/0.0110877925398804 if province_code==460000 & year==1990
 24. replace `X' = `X'/0.00980917665888599 if province_code==500000 & year==1990
 25. replace `X' = `X'/0.00980917665888599 if province_code==510000 & year==1990
 26. replace `X' = `X'/0.00961913386857969 if province_code==520000 & year==1990
 27. replace `X' = `X'/0.0101087805269901 if province_code==530000 & year==1990
 28. replace `X' = `X'/0.0110086019644719 if province_code==540000 & year==1990
 29. replace `X' = `X'/0.0103511899662564 if province_code==610000 & year==1990
 30. replace `X' = `X'/0.0108154966257644 if province_code==620000 & year==1990
 31. replace `X' = `X'/0.0128754084074611 if province_code==630000 & year==1990
 32. replace `X' = `X'/0.0083057473916061 if province_code==640000 & year==1990
 33. replace `X' = `X'/0.0102265947680152 if province_code==650000 & year==1990
 34. 
. **** Use 100% counts as weights in 2000, excluding special districts
. replace `X' = `X'*actpop/sampop if year==2000 & sampop~=0
 35. 
. *** Loren's programs create a uniform 0.2% sample, so multiply by 500
. replace `X' = `X'*500 if year==2005
 36. }
(2,352 real changes made)
(18 real changes made)
(18 real changes made)
(167 real changes made)
(118 real changes made)
(0 real changes made)
(97 real changes made)
(59 real changes made)
(115 real changes made)
(21 real changes made)
(102 real changes made)
(87 real changes made)
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(76 real changes made)
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(133 real changes made)
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(92 real changes made)
(116 real changes made)
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(0 real changes made)
(38 real changes made)
(167 real changes made)
(86 real changes made)
(61 real changes made)
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(0 real changes made)
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(0 real changes made)
(2,466 real changes made)
(2,463 real changes made)
(2,352 real changes made)
(18 real changes made)
(18 real changes made)
(167 real changes made)
(118 real changes made)
(0 real changes made)
(97 real changes made)
(59 real changes made)
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(102 real changes made)
(87 real changes made)
(92 real changes made)
(76 real changes made)
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(133 real changes made)
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(92 real changes made)
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(2,466 real changes made)
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(2,461 real changes made)
(2,368 real changes made)
(1,907 real changes made)
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(2,088 real changes made)
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(0 real changes made)
(2,455 real changes made)
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(18 real changes made)
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(0 real changes made)
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(0 real changes made)
(2,451 real changes made)
(2,324 real changes made)
(2,036 real changes made)
(18 real changes made)
(18 real changes made)
(122 real changes made)
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(79 real changes made)
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(33 real changes made)
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(85 real changes made)
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(16 real changes made)
(0 real changes made)
(53 real changes made)
(26 real changes made)
(0 real changes made)
(3 real changes made)
(0 real changes made)
(2,194 real changes made)
(1,483 real changes made)
(2,275 real changes made)
(18 real changes made)
(18 real changes made)
(144 real changes made)
(87 real changes made)
(0 real changes made)
(94 real changes made)
(53 real changes made)
(86 real changes made)
(21 real changes made)
(100 real changes made)
(83 real changes made)
(76 real changes made)
(62 real changes made)
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(123 real changes made)
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(96 real changes made)
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(0 real changes made)
(69 real changes made)
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(0 real changes made)
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(2,396 real changes made)
(1,824 real changes made)
(1,578 real changes made)
(18 real changes made)
(18 real changes made)
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(54 real changes made)
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(0 real changes made)
(34 real changes made)
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(0 real changes made)
(1,913 real changes made)
(1,151 real changes made)
(2,344 real changes made)
(18 real changes made)
(18 real changes made)
(151 real changes made)
(105 real changes made)
(0 real changes made)
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(56 real changes made)
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(74 real changes made)
(85 real changes made)
(126 real changes made)
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(100 real changes made)
(65 real changes made)
(0 real changes made)
(9 real changes made)
(0 real changes made)
(2,463 real changes made)
(2,379 real changes made)
(2,275 real changes made)
(18 real changes made)
(18 real changes made)
(127 real changes made)
(78 real changes made)
(0 real changes made)
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(92 real changes made)
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(83 real changes made)
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(65 real changes made)
(120 real changes made)
(105 real changes made)
(78 real changes made)
(89 real changes made)
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(42 real changes made)
(0 real changes made)
(32 real changes made)
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. 
. **** Drop unneeded variables
. drop GbProv-County_EN mrg_* pgdp-nmhanzi countyCode ruralMigA1-urbanMig19to55Hig
> hA1 expnd-totemp_sect3 lasset_n_qz

. 
. sort unit_code_08 year

. save ..\..\data\tabular_data_BJ\generated\us123-census.dta, replace
file ..\..\data\tabular_data_BJ\generated\us123-census.dta saved

. 
. 
. 
. 
. erase temp_census.dta

. erase temp.dta

. erase cnt2000_temp.dta

. erase cnt2005_temp.dta

. erase cnt2010_temp.dta

. erase cen0005.dta

. erase temp_1.dta

. erase temp82.dta

. erase temp820005.dta

. erase tempcen90.dta

. erase 2010CountyCensusA.dta

. erase 2010CountyCensusL.dta

. 
end of do-file

. do tabdata4.do

. /** tabdata4.do
> 
> This do-file takes disaggregated data from various sources including the census
> and aggregates it into city proper, prefecture and various other levels of aggre
> gation.
> 
> **/
. 
. 
. clear

. set mem 100m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are
    performed on the fly automatically.

. set more off

. capture log close
